def generate_gpmcc_posteriors(cctype, distargs, D_train, iters, seconds):
"""Learns gpmcc on D_train for seconds and simulates NUM_TEST times."""
# Learning and posterior simulation.
engine = Engine(D_train,
cctypes=[cctype],
distargs=[distargs],
num_states=64,
rng=gu.gen_rng(1))
engine.transition(N=iters, S=seconds, progress=0)
if iters:
kernel = 'column_params' if cu.cctype_class(cctype).is_conditional()\
else 'column_hypers'
engine.transition(N=100, kernels=[kernel], progress=0)
samples = engine.simulate(-1, [0], N=NUM_TEST)
marginals = engine.logpdf_score()
ranking = np.argsort(marginals)[::-1]
for r in ranking[:5]:
engine.get_state(r).plot()
return [samples[i] for i in ranking[:5]]
def state():
# Create an engine.
engine = Engine(DATA,
cctypes=['normal', 'categorical'],
distargs=[None, {
'k': 6
}],
num_states=4,
rng=gu.gen_rng(212))
engine.transition(N=15)
marginals = engine.logpdf_score()
ranking = np.argsort(marginals)[::-1]
return engine.get_state(ranking[0])
def run_test(args):
n_rows = args["num_rows"]
n_iters = args["num_iters"]
n_chains = args["num_chains"]
n_per_chain = int(float(n_rows) / n_chains)
fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(16, 9))
axes = axes.ravel()
k = 0
for shape in shapes:
print "Shape: %s" % shape
T_o = np.asarray(gen_function[shape](n_rows))
T_i = []
engine = Engine(T_o.T,
cctypes=cctypes,
distargs=distargs,
num_states=n_chains)
engine.transition(N=n_iters)
for chain in xrange(n_chains):
state = engine.get_state(chain)
print "chain %i of %i" % (chain + 1, n_chains)
T_i.extend(state.simulate(-1, [0, 1], N=n_per_chain))
T_i = np.array(T_i)
ax = axes[k]
ax.scatter(T_o[0], T_o[1], color='blue', edgecolor='none')
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_title("%s original" % shape)
ax = axes[k + 4]
ax.scatter(T_i[:, 0], T_i[:, 1], color='red', edgecolor='none')
ax.set_xlabel("X")
ax.set_ylabel("Y")
ax.set_xlim(ax.get_xlim())
ax.set_ylim(ax.get_ylim())
ax.set_title("%s simulated" % shape)
k += 1
print "Done."
return fig
def test_serialize_composite_cgpm():
rng = gu.gen_rng(2)
# Generate the data.
cctypes, distargs = cu.parse_distargs([
'categorical(k=3)', # RandomForest 0
'normal', # LinearRegression 1
'categorical(k=3)', # GPMCC 2
'poisson', # GPMCC 3
'normal', # GPMCC 4
'lognormal' # GPMCC 5
])
T, Zv, Zc = tu.gen_data_table(
35, [.4, .6], [[.33, .33, .34], [.5, .5]],
cctypes, distargs, [.2]*len(cctypes), rng=rng)
D = np.transpose(T)
# Create GPMCC.
state = State(
D[:,2:], outputs=[2,3,4,5], cctypes=cctypes[2:],
distargs=distargs[2:], rng=rng)
# Create a Forest.
forest = RandomForest(
outputs=[0],
inputs=[1,2,3,4],
distargs={
'inputs': {
'stattypes': [cctypes[i] for i in [1,2,3,4]],
'statargs': [distargs[i] for i in [1,2,3,4]]},
'k': distargs[0]['k']},
rng=rng)
# Create a Regression.
linreg = LinearRegression(
outputs=[1],
inputs=[3,4,5],
distargs={
'inputs': {
'stattypes': [cctypes[i] for i in [3,4,5]],
'statargs': [distargs[i] for i in [3,4,5]]}},
rng=rng)
# Incorporate the data.
def incorporate_data(cgpm, rowid, row):
cgpm.incorporate(
rowid,
{i: row[i] for i in cgpm.outputs},
{i: row[i] for i in cgpm.inputs},
)
for rowid, row in enumerate(D):
incorporate_data(forest, rowid, row)
incorporate_data(linreg, rowid, row)
# Compose the CGPMs.
# Run state transitions.
state.transition(N=10, progress=False)
# Compose CGPMs, instructing State to run the transitions.
token_forest = state.compose_cgpm(forest)
token_linreg = state.compose_cgpm(linreg)
state.transition_foreign(N=10, cols=[forest.outputs[0], linreg.outputs[0]])
# Now run the serialization.
metadata = state.to_metadata()
state2 = State.from_metadata(metadata)
# Check that the tokens are in state2.
assert token_forest in state2.hooked_cgpms
assert token_linreg in state2.hooked_cgpms
# The hooked cgpms must be unique objects after serialize/deserialize.
assert state.hooked_cgpms[token_forest] != state2.hooked_cgpms[token_forest]
assert state.hooked_cgpms[token_linreg] != state2.hooked_cgpms[token_linreg]
# Check that the log scores of the hooked cgpms agree.
assert np.allclose(
state.hooked_cgpms[token_forest].logpdf_score(),
state2.hooked_cgpms[token_forest].logpdf_score())
assert np.allclose(
state.hooked_cgpms[token_linreg].logpdf_score(),
state2.hooked_cgpms[token_linreg].logpdf_score())
# Now run some tests for the engine.
e = Engine(
D[:,2:], outputs=[2,3,4,5], cctypes=cctypes[2:],
distargs=distargs[2:], num_states=2, rng=rng)
e.compose_cgpm([forest, forest], multiprocess=1)
e.compose_cgpm([linreg, linreg], multiprocess=1)
e.transition_foreign(N=1, cols=[forest.outputs[0], linreg.outputs[0]])
e.dependence_probability(0,1)
e.simulate(-1, [0,1], {2:1}, multiprocess=0)
e.logpdf(-1, {1:1}, {2:1, 0:0}, multiprocess=0)
state3 = e.get_state(0)
# There is no guarantee that the logpdf score improves with inference, but
# it should reduce by more than a few nats.
def check_logpdf_delta(before, after):
return before < after or (after-before) < 5
check_logpdf_delta(
before=state.hooked_cgpms[token_forest].logpdf_score(),
after=state3.hooked_cgpms[token_forest].logpdf_score())
check_logpdf_delta(
before=state.hooked_cgpms[token_linreg].logpdf_score(),
after=state3.hooked_cgpms[token_linreg].logpdf_score())
